Missing Data Evaluation in Financial Time Series
Abstract
This paper examines and compares various methods for handling missing data in financial time series and panel data. The study focuses on both simple and advanced imputation techniques, including last value carried forward (LVCF), multivariate regression, autoregressive models, non-parametric methods, and neural networks.
Special attention is given to the role of time dependencies and lagged variables, which allow for improved estimation compared to purely cross-sectional approaches. The methods are evaluated based on their ability to preserve statistical properties, maintain relationships between variables, and produce realistic estimates.
The findings highlight that more complex methods, such as autoregressive models and multilayer perceptrons, generally provide better results, although they require greater computational resources. The paper concludes that the choice of method depends on data characteristics, missing data patterns, and research objectives.
References
Chambers, R. (2003). Evaluation Criteria for Statistical Editing and Imputation.
Little, R. J. A., & Rubin, D. B. (2002). Statistical Analysis with Missing Data.
Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys.
Rubin, D. (1996). Multiple imputation after 18+ years.
Scheffer, J. (2002). Dealing with Missing Data.
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